Atmospheric pollution change trend correction method

Abstract

The invention discloses a correction method for an atmospheric pollution change trend. The correction method comprises the following steps: acquiring atmospheric pollutant data and corresponding meteorological data of an atmospheric environment within a preset time duration; carrying out gradient packaging on the atmospheric pollutant data and the corresponding meteorological data, and obtaining the change of the atmospheric pollutant data in each gradient; and carrying out all gradient summation and re-averaging processing on the change condition of the atmospheric pollutant data caused by the meteorological data to obtain the change condition of the atmospheric pollutant data corrected based on the meteorological data so as to realize correction of the change trend of atmospheric pollution. According to the method, the change trend of the atmospheric pollutant data can be effectively corrected, so that the accurate change trend of the atmospheric pollutant data is obtained; therefore, more accurate and reliable data support is provided for the research of the atmospheric environment, and the problems in the prior art are effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent environmental monitoring, and particularly to a method for correcting the changing trend of air pollution. Background Art

[0002] In order to better protect the atmospheric environment, it is necessary to understand the long-term evolution trend of air pollutants. The changing trend of air pollutants is mainly affected by the emissions of human activities. Currently, the long-term evolution trend of air pollutants is usually predicted based on the emissions of human activities, and strict emission standards and air treatment policies are formulated based on this to reduce air pollutants, thereby improving the atmospheric environment for people's lives.

[0003] However, in addition to being directly affected by the emissions of human activities, some pollutants in the atmospheric environment are also affected by biomass emissions and meteorological factors. For example, for air pollutants such as formaldehyde and glyoxal generated secondarily in the atmosphere, meteorological elements such as temperature and air pressure can cause changes in biomass emissions, thereby affecting the long-term changing trend of air pollutant observation results, and further affecting the judgment of environmental protection workers on the long-term evolution trend of air pollutants.

[0004] Currently, there is no implementation solution that can accurately predict the long-term evolution trend of air pollutants, so it is impossible to protect the atmospheric environment based on the accurate long-term evolution trend of air pollutants.

[0005] In view of this, the present invention is specifically proposed. Summary of the Invention

[0006] The object of the present invention is to provide a method for correcting the changing trend of air pollution to improve the accuracy of predicting the changing trend of air pollution and solve the problems existing in the prior art.

[0007] The object of the present invention is achieved by the following technical solutions:

[0008] A method for correcting the changing trend of air pollution includes:

[0009] Obtaining air pollutant data and corresponding meteorological data of the atmospheric environment within a predetermined time length;

[0010] Performing gradient encapsulation on the air pollutant data and the corresponding meteorological data, and obtaining the changes in the air pollutant data within each gradient;

[0011] Performing summation and averaging processing on all gradients for the situation of the changes in the air pollutant data caused by the meteorological data to obtain the changes in the air pollutant data corrected based on the meteorological data, so as to achieve the correction of the changing trend of air pollution.

[0012] The gradient encapsulation includes:

[0013] Rearranging and encapsulating the atmospheric pollutant data and the corresponding meteorological data according to the gradients of the predetermined meteorological data, and within each gradient of the meteorological data, dividing the atmospheric pollutant data into multiple stages according to the observation time, and calculating the degree of change of the atmospheric pollutant data between the multiple stages to obtain the result after gradient encapsulation; wherein, the gradient of the meteorological data refers to the result obtained by dividing the meteorological data according to a predetermined rule.

[0014] The calculation process of the degree of change of the atmospheric pollutant data between the multiple stages includes:

[0015] Calculating the average value of the atmospheric pollutant data within each stage, and calculating and determining the degree of change of the atmospheric pollutant data between each stage based on the average value.

[0016] The step of performing the summation and averaging processing for all gradients includes:

[0017] For the observed pixels of the atmospheric pollutant data, calculating its long-term change amount ΔC after calibration processing based on the meteorological data p The formula for which includes:

[0018]

[0019] wherein, N p,t,1 and N p,t,2 represent the number of all observed data for the first stage and the second stage within the pixel p and the meteorological data gradient t, and ΔC p,t is the average change value of the atmospheric pollutant data between the two stages, and its calculation formula is:

[0020]

[0021] wherein, and represent the average value of the atmospheric pollutant data in the first stage and the average value of the atmospheric pollutant data in the second stage.

[0022] This method further includes:

[0023] Calculating and obtaining the interannual growth rate of the atmospheric pollutant that is not affected by seasons, and correcting the changing trend of the air pollution based on the interannual growth rate of the atmospheric pollutant that is not affected by seasons.

[0024] The calculation method of the interannual growth rate of the atmospheric pollutant that is not affected by seasons includes:

[0025] Constructing the seasonal change interannual growth rate model as:

[0026]

[0027] Among them, M(t) represents the average atmospheric pollutant concentration obtained by observation within time t, t is the observation time, and A, B, C n , D n are all fitting parameters, where C n , D n are the parameter terms for fitting the quarterly variation, A is the average value of the pollutant concentration in the first year, and B is the annual growth rate of the atmospheric pollutant to be obtained;

[0028] Input the atmospheric pollutant data obtained from the observation results into the seasonal variation and interannual growth rate model, and perform least squares fitting on each parameter term in the seasonal variation and interannual growth rate model to obtain the interannual growth rate of atmospheric pollutants that is not affected by seasons.

[0029] Compared with the prior art, a method for correcting the changing trend of air pollution provided by the present invention can correct the long-term changing trend of atmospheric pollutants by using multi-source meteorological data, and then obtain an implementation scheme capable of accurately predicting the changing trend of air pollution. Specifically, it uses atmospheric pollutant data and the matching meteorological data as basic data to analyze and calculate the changing trend of atmospheric pollutant data, and then effectively corrects the changing trend of atmospheric pollutant data to obtain the long-term changing trend of accurate atmospheric pollutant data. The implementation of the present invention provides more accurate and reliable data support for the research of the atmospheric environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0031] Figure 1 It is a schematic diagram of the implementation process of the method provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention; obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments, which do not constitute a limitation to the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the protection scope of the present invention.

[0033] First, the terms that may be used in this article are described as follows:

[0034] The term "and / or" means that either or both of the two can be achieved. For example, X and / or Y means that it includes three cases: the case of "X" or "Y" and the case of "X and Y".

[0035] Descriptions with terms such as "comprising", "including", "containing", "having" or other similar semantics should be interpreted as non-exclusive inclusion. For example: including a certain technical feature element (such as raw material, component, ingredient, carrier, dosage form, material, size, part, component, mechanism, device, step, process, method, reaction condition, processing condition, parameter, algorithm, signal, data, product or article, etc.) should be interpreted as not only including the explicitly listed certain technical feature element, but also including other technical feature elements well-known in the art that are not explicitly listed.

[0036] The term "consisting of" means excluding any technical feature element that is not explicitly listed. If this term is used in a claim, it will make the claim a closed type, so that it does not include technical feature elements other than the explicitly listed ones, except for conventional impurities related thereto. If this term only appears in a sub-clause of a claim, then it only limits the elements explicitly listed in that sub-clause, and the elements recorded in other sub-clauses are not excluded from the overall claim.

[0037] The term "parts by mass" represents the mass ratio relationship between multiple components. For example: if it is described that component X is x parts by mass and component Y is y parts by mass, then it means that the mass ratio of component X to component Y is x:y; 1 part by mass can represent any mass. For example: 1 part by mass can be expressed as 1 kg or 3.1415926 kg, etc. The sum of the parts by mass of all components is not necessarily 100 parts, and it can be greater than 100 parts, less than 100 parts or equal to 100 parts. Unless otherwise specified, the parts, ratios and percentages described in this article are by mass.

[0038] Unless otherwise clearly specified or limited, terms such as "installed", "connected", "joined", "fixed", etc. should be understood in a broad sense. For example: it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in this article can be understood according to the specific circumstances.

[0039] When concentration, temperature, pressure, size or other parameters are expressed in the form of a numerical range, the numerical range should be understood as specifically disclosing all ranges formed by the pairing of any upper limit value, lower limit value, and preferred value within the numerical range, regardless of whether the range is explicitly recorded; for example, if the numerical range "2 to 8" is recorded, then this numerical range should be interpreted as including ranges such as "2 to 7", "2 to 6", "5 to 7", "3 to 4 and 6 to 7", "3 to 5 and 7", "2 and 5 to 7", etc. Unless otherwise specified, the numerical ranges recorded herein include both their end values and all integers and fractions within the numerical range.

[0040] The orientation or positional relationship indicated by terms such as "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of description and to simplify the description, rather than explicitly or implicitly indicating that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to this article.

[0041] The following provides a detailed description of a method for correcting the changing trend of air pollution provided by the present invention. The content not described in detail in the embodiments of the present invention belongs to the prior art well-known to those skilled in the art. For those conditions not specified in the embodiments of the present invention, they are carried out according to the conventional conditions in the art or the conditions recommended by the manufacturer. For the reagents or instruments not indicating the manufacturer in the embodiments of the present invention, they are all conventional products that can be obtained through commercial purchase.

[0042] In the implementation process of the present invention, it is found that correcting the long-term changing trend of air pollutants in satellite observations and ground observations based on multiple meteorological elements has an important guiding role in evaluating the impact of emission reduction measures on anthropogenic air pollutants. Based on this, the embodiments of the present invention provide a method for correcting the changing trend of air pollution. In this method, an algorithm for correcting the long-term changing trend of air pollutants using multiple meteorological data is mainly provided, and thus an accurate changing trend of air pollution can be corrected and obtained.

[0043] Specifically, a method for correcting the long-term changing trend of air pollutants provided by the embodiments of the present invention, referring to Figure 1 as shown, the specific implementation process may include the following processing steps:

[0044] Step 11, acquisition and utilization of air pollutant data;

[0045] Specifically, the corresponding air pollutant data mainly includes the concentration information of secondary generated pollutants, which may include the concentration information of glyoxal, formaldehyde, and secondary organic aerosol, etc.;

[0046] In the embodiments of the present invention, for the atmospheric pollutant concentration information observed based on different platforms, it can be used for analyzing the long-term change trend of atmospheric pollutants; among them, the corresponding observation platforms can be the tropospheric column concentration results of spaceborne observations, or the point-type or vertical profile results of ground observations, and so on;

[0047] Moreover, since it is necessary to accurately correct the long-term change trend of atmospheric pollutants, therefore, it is required that the corresponding air pollution data should have long-term continuity, that is, the data obtained through long-term observations can truly reflect the accurate long-term change trend of atmospheric pollutants. For example, the corresponding air pollution data needs to have observation results for more than 2 years, and the time resolution is at least daily, and the corresponding missing values do not exceed 20%, etc.

[0048] Step 12, obtaining meteorological data;

[0049] In the embodiments of the present invention, the collected and obtained meteorological data will also be applied to the correction process. The corresponding meteorological data mainly can but is not limited to include air temperature, air pressure, wind, sunshine intensity, humidity, etc.;

[0050] In the implementation process of the present invention, it is found that for the secondary-generated pollutants, the elements contained in the meteorological data will affect some biomass emissions. For example, when the air temperature rises, the sunshine increases, the biological activity increases, and the emissions of substances such as isoprene and monoterpenes increase, the secondary-generated formaldehyde, glyoxal, and secondary organic aerosols will also increase, thus affecting the trend judgment of anthropogenic source pollutants. Therefore, in the implementation process of the embodiments of the present invention, it is also necessary to obtain the corresponding meteorological data as one of the basic data for correction;

[0051] Specifically, for each observation result (i.e., atmospheric pollutant data), corresponding meteorological data should be paired, that is, for obtaining the atmospheric pollutant data, it is also necessary to obtain the meteorological data that matches it. That is, the matching means the atmospheric pollutant data and its corresponding meteorological data under the same spatio-temporal conditions; for example, for the pollutant grid results observed by satellite, corresponding near-surface average meteorological data (such as meteorological data from 1000 hPa to 900 hPa) should be paired within each observation grid, and this meteorological data can be the gridded results of model simulation; for the special data of ground-observed air pollution, corresponding meteorological elements (i.e., meteorological data) should be paired at each observation moment, which can be the measured meteorological elements near the observation site or the simulation results as the corresponding meteorological data.

[0052] Step 13, encapsulating and redistributing the observation results of atmospheric pollutant data and meteorological data;

[0053] In this step, it is necessary to perform gradient encapsulation on the atmospheric pollutant data and the corresponding meteorological data, and obtain the changes in the atmospheric pollutant data within each gradient; that is, redistribute and encapsulate the obtained atmospheric pollutant data into the meteorological data of different gradients;

[0054] That is, perform gradient encapsulation on the observed atmospheric pollutant data and the corresponding meteorological data one by one; in the actual processing process, all the observation results and the corresponding meteorological data can be arranged and encapsulated in ascending order and fixed gradients to obtain the changes in the atmospheric pollutant data within each gradient, such as the changes in the concentration of atmospheric pollutants, etc.;

[0055] Specifically, the corresponding gradient encapsulation (including redistribution) process may include: rearranging and encapsulating the atmospheric pollutant data and the corresponding meteorological data according to the predetermined meteorological data gradients to achieve the corresponding encapsulation and redistribution; moreover, within each gradient of the meteorological data, the atmospheric pollutant data is divided into multiple stages according to the observation time, and the degree of change in the atmospheric pollutant data between the multiple stages is calculated to obtain the result after gradient encapsulation; where the gradient of the meteorological data refers to the result obtained by dividing the meteorological data according to the predetermined rules, and the result includes multiple segments of the meteorological data that make up the meteorological data;

[0056] In the above processing process, the calculation process of the degree of change in the atmospheric pollutant data between the multiple stages may include: calculating the average value of the atmospheric pollutant data within each stage, and calculating and determining the degree of change in the atmospheric pollutant data between the respective stages based on the average value.

[0057] For example, taking the grid data observed by satellites as the observation result and the temperature data as the meteorological data as an example, for each satellite pixel p, all the long-term observed atmospheric pollutant concentration results (i.e., the observation results of the atmospheric pollutant data) and the corresponding temperature results (i.e., the meteorological data) can be arranged and encapsulated according to the temperature gradient from 285K to 320K, with a gradient of 0.25K for each; for each temperature gradient t, all the long-term observation results can be divided into two or more stages. For example, taking it as divided into two stages, for the observation results from 2020 to 2025, it can be divided into stage 1: 2020 - 2022 and stage 2: 2023 - 2025. Then, for this temperature gradient t of this pixel p, the average change ΔC of its atmospheric pollutants between the two stages p,t (i.e., the degree of change in the atmospheric pollutant data between the stages) can be calculated by the following formula, that is:

[0058]

[0059] Where, and Denote the average of the air pollutant data in the first stage and the average of the air pollutant data in the second stage, that is, for pixel p, it is the average pollutant concentration in the two stages of the temperature gradient t respectively.

[0060] Step 14, based on the variation of the air pollutant data corrected by meteorological elements (i.e., meteorological data), that is, the variation result of the corrected air pollutant concentration;

[0061] For all the variations of the air pollutant data (such as air pollutant concentration) caused by meteorological elements (i.e., the meteorological data), perform summation and averaging for all gradients to obtain the variation of the air pollutant data corrected based on the meteorological elements, or the variation result;

[0062] Specifically, taking satellite observation data as air pollutant data and temperature data as meteorological data as an example, for pixel p of the satellite observation data, its long-term variation ΔC after correction processing based on temperature data p can be calculated by the following formula, that is:

[0063]

[0064] where N p,t,1 and N p,t,2 represent the number of all observation values in the first stage and the second stage within pixel p and temperature gradient t. To ensure data quality and calculation effectiveness, the minimum number of meteorological element gradients should not be less than 20, and the number of observation results within each gradient range should not be less than 30;

[0065] For the ground observation results, the corresponding pixel p can correspond to the ground observation point, and the corresponding calculation formula is the same as the above calculation formula;

[0066] Moreover, during the processing of this step, in addition to temperature data, corresponding meteorological elements such as air pressure, humidity, and sunshine intensity can all adopt the above processing method to correct the long-term variation of the observation data.

[0067] Step 15, calculation of the change in the annual growth rate of air pollutants;

[0068] For the secondary generated air pollutants affected by meteorological elements, their seasonal variations are significant, and different pollutants will show different variation characteristics. For example, the formaldehyde concentration shows the characteristics of high concentration in summer and low concentration in winter; however, the formaldehyde emissions from anthropogenic sources often do not have such seasonal variation characteristics. To obtain the long-term variation trend of air pollutants not affected by seasonal variations, that is, the annual growth rate of air pollutants, the processing process adopted in the embodiments of the present invention includes:

[0069] Construct an annual growth rate model for seasonal variation, i.e.:

[0070]

[0071] Among them, M(t) represents the average concentration of atmospheric pollutants in the observed time period t (such as within a month), t is the observed time, and A, B, C n , D n are all fitting parameters. Among them, C n , D n are the parameter terms for fitting quarterly variations. A is the average value of the pollutant concentration in the first year, and B is the annual growth rate of the atmospheric pollutants to be obtained;

[0072] Furthermore, by inputting the observed results into the above M(t) model and performing least squares fitting on each parameter term in the M(t) model, the final annual growth rate of atmospheric pollutants that is not affected by seasonal variation can be calculated and obtained;

[0073] In this way, the changing trend of air pollution can be corrected based on the annual growth rate of atmospheric pollutants that is not affected by seasonal variation, and then an accurate changing trend of air pollution can be obtained.

[0074] In summary, in the implementation process of the above technical solution provided by the present invention, since atmospheric pollutant data and the matching meteorological data are used as basic data for analyzing and calculating the changing trend of atmospheric pollutant data, the changing trend of atmospheric pollutant data is effectively corrected to obtain the long-term changing trend of accurate atmospheric pollutant data. The implementation of the present invention provides more accurate and reliable data support for the research of the atmospheric environment, and effectively solves the problem of inaccurate prediction of the changing trend of air pollution existing in the prior art.

[0075] The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims. The information disclosed in the background art part of this article is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as an admission or any form of suggestion that this information constitutes the prior art known to those skilled in the art.

Claims

1. A method for correcting the trend of air pollution, characterized in that: include: Acquiring atmospheric pollutant data and corresponding meteorological data of the atmospheric environment within a predetermined time period; Gradient encapsulation is performed on the atmospheric pollutant data and the corresponding meteorological data, and changes in the atmospheric pollutant data within each gradient are obtained; All gradients are summed and averaged for changes in the atmospheric pollutant data caused by the meteorological data to obtain changes in the atmospheric pollutant data corrected based on the meteorological data, so as to achieve correction of the atmospheric pollution change trend.

2. The method according to claim 1, characterized in that The gradient closure comprises: The atmospheric pollutant data and the corresponding meteorological data are rearranged and packaged according to a predetermined gradient of the meteorological data, and within the gradient of each meteorological data, the atmospheric pollutant data are divided into multiple stages according to the time of observation, and the degree of change of the atmospheric pollutant data between the multiple stages is calculated to obtain the result after gradient packaging; wherein the gradient of the meteorological data refers to the result obtained after dividing the meteorological data according to a predetermined rule.

3. The method according to claim 2, characterized in that The calculation process of the degree of change of the atmospheric pollutant data between the multiple stages includes: The average value of the air pollutant data in each stage is calculated, and the degree of change of the air pollutant data between each stage is determined based on the average value.

4. The method according to claim 1, 2 or 3, characterized in that: The step of performing the summing and averaging of all gradients comprises: For the observed pixels of the atmospheric pollutant data, calculate the long-term change ΔC after calibration based on the meteorological data. p The formulas include: Among them, N p,t,1 and N p,t,2 represents the number of all observations in the first and second stages for pixel p and meteorological data gradient t, ΔC p,t is the average change value of air pollutant data in two stages, and its calculation formula is: in, and It is expressed as the average of the atmospheric pollutant data of the first stage and the average of the atmospheric pollutant data of the second stage.

5. The method according to claim 1, 2 or 3, characterized in that: The method further includes: The annual growth rate of atmospheric pollutants not affected by seasons is calculated and obtained, and the trend of atmospheric pollution changes is corrected based on the annual growth rate of atmospheric pollutants not affected by seasons.

6. The method according to claim 5, characterized in that The calculation methods for the annual growth rate of atmospheric pollutants not affected by seasons include: The model of the annual growth rate of seasonal variation is constructed as follows: Where M(t) represents the average concentration of atmospheric pollutants in the observed time t, t is the observation time, A, B, C n ,D n are all fitting parameters, among which C n ,D n is the parameter item for fitting quarterly changes, A is the mean value of pollutant concentration in the first year, and B is the required annual growth rate of atmospheric pollutants; The atmospheric pollutant data obtained from the observation results are input into the seasonal variation annual growth rate model, and the least squares fitting process is performed on each parameter item in the seasonal variation annual growth rate model to obtain the atmospheric pollutant annual growth rate that is not affected by the season.

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